1,011,564 research outputs found
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference âOptimisation of Mobile Communication Networksâ focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Knowledge Flows through Informal Contacts in Industrial Clusters Myths or Realities?
The role of informal networks in the development of regional clusters has received a lot of attention in the literature recently. Informal contact between employees in different firms is argued to be one of the main carriers of knowledge between firms in a cluster. This paper empirically examines the role of informal contacts in a specific cluster. In a recent questionnaire, we ask a sample of engineers in a regional cluster of wireless communication firms in Northern Denmark, a series of questions on informal networks. We analyze whether the engineers actually acquire valuable knowledge through these networks. We find that the engineers do share even valuable knowledge with informal contacts. This shows that informal contacts are important channels of knowledge diffusion.Informal contacts, regional clusters, communication technology
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Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions.
Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here, we make use of a framework that treats intracellular signal transduction networks as "black boxes" with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps
Scheduling with Rate Adaptation under Incomplete Knowledge of Channel/Estimator Statistics
In time-varying wireless networks, the states of the communication channels
are subject to random variations, and hence need to be estimated for efficient
rate adaptation and scheduling. The estimation mechanism possesses inaccuracies
that need to be tackled in a probabilistic framework. In this work, we study
scheduling with rate adaptation in single-hop queueing networks under two
levels of channel uncertainty: when the channel estimates are inaccurate but
complete knowledge of the channel/estimator joint statistics is available at
the scheduler; and when the knowledge of the joint statistics is incomplete. In
the former case, we characterize the network stability region and show that a
maximum-weight type scheduling policy is throughput-optimal. In the latter
case, we propose a joint channel statistics learning - scheduling policy. With
an associated trade-off in average packet delay and convergence time, the
proposed policy has a stability region arbitrarily close to the stability
region of the network under full knowledge of channel/estimator joint
statistics.Comment: 48th Allerton Conference on Communication, Control, and Computing,
Monticello, IL, Sept. 201
Navigation of brain networks
Understanding the mechanisms of neural communication in large-scale brain
networks remains a major goal in neuroscience. We investigated whether
navigation is a parsimonious routing model for connectomics. Navigating a
network involves progressing to the next node that is closest in distance to a
desired destination. We developed a measure to quantify navigation efficiency
and found that connectomes in a range of mammalian species (human, mouse and
macaque) can be successfully navigated with near-optimal efficiency (>80% of
optimal efficiency for typical connection densities). Rewiring network topology
or repositioning network nodes resulted in 45%-60% reductions in navigation
performance. Specifically, we found that brain networks cannot be progressively
rewired (randomized or clusterized) to result in topologies with significantly
improved navigation performance. Navigation was also found to: i) promote a
resource-efficient distribution of the information traffic load, potentially
relieving communication bottlenecks; and, ii) explain significant variation in
functional connectivity. Unlike prevalently studied communication strategies in
connectomics, navigation does not mandate biologically unrealistic assumptions
about global knowledge of network topology. We conclude that the wiring and
spatial embedding of brain networks is conducive to effective decentralized
communication. Graph-theoretic studies of the connectome should consider
measures of network efficiency and centrality that are consistent with
decentralized models of neural communication
Distributed Broadcasting and Mapping Protocols in Directed Anonymous Networks
We initiate the study of distributed protocols over directed anonymous networks that are not necessarily strongly connected. In such networks, nodes are aware only of their incoming and outgoing edges, have no unique identity, and have no knowledge of the network topology or even bounds on its parameters, like the number of nodes or the network diameter. Anonymous networks are of interest in various settings such as wireless ad-hoc networks and peer to peer networks. Our goal is to create distributed protocols that reduce the uncertainty by distributing the knowledge of the network topology to all the nodes.
We consider two basic protocols: broadcasting and unique label assignment. These two protocols enable a complete mapping of the network and can serve as key building blocks in more advanced protocols. We develop distributed asynchronous protocols as well as derive lower bounds on their communication complexity, total bandwidth complexity, and node label complexity. The resulting lower bounds are sometimes surprisingly high, exhibiting the complexity of topology extraction in directed anonymous networks
Symmetric and Synchronous Communication in Peer-to-Peer Networks
Motivated by distributed implementations of game-theoretical algorithms, we
study symmetric process systems and the problem of attaining common knowledge
between processes. We formalize our setting by defining a notion of
peer-to-peer networks(*) and appropriate symmetry concepts in the context of
Communicating Sequential Processes (CSP), due to the common knowledge creating
effects of its synchronous communication primitives. We then prove that CSP
with input and output guards makes common knowledge in symmetric peer-to-peer
networks possible, but not the restricted version which disallows output
statements in guards and is commonly implemented.
(*) Please note that we are not dealing with fashionable incarnations such as
file-sharing networks, but merely use this name for a mathematical notion of a
network consisting of directly connected peers "treated on an equal footing",
i.e. not having a client-server structure or otherwise pre-determined roles.)Comment: polished, modernized references; incorporated referee feedback from
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